In Physical Human--Robot Interaction (pHRI) grippers, humans and robots may contribute simultaneously to actions, so it is necessary to determine how to combine their commands. Control may be swapped from one to the other within certain limits, or input commands may be combined according to some criteria. The Assist-As-Needed (AAN) paradigm focuses on this second approach, as the controller is expected to provide the minimum required assistance to users. Some AAN systems rely on predicting human intention to adjust actions. However, if prediction is too hard, reactive AAN systems may weigh input commands into an emergent one. This paper proposes a novel AAN reactive control system for a robot gripper where input commands are weighted by their respective local performances. Thus, rather than minimizing tracking errors or differences to expected velocities, humans receive more help depending on their needs. The system has been tested using a gripper attached to a sensitive robot arm, which provides evaluation parameters. Tests consisted of completing an on-air planar path with both arms. After the robot gripped a person's forearm, the path and current position of the robot were displayed on a screen to provide feedback to the human. The proposed control has been compared to results without assistance and to impedance control for benchmarking. A statistical analysis of the results proves that global performance improved and tracking errors decreased for ten volunteers with the proposed controller. Besides, unlike impedance control, the proposed one does not significantly affect exerted forces, command variation, or disagreement, measured as the angular difference between human and output command. Results support that the proposed control scheme fits the AAN paradigm, although future work will require further tests for more complex environments and tasks.

翻译：在物理人机交互（pHRI）抓取器中，人类和机器人可能同时参与动作，因此需要确定如何组合它们的指令。控制可在一定范围内相互切换，或者输入指令可根据某些标准进行组合。“按需辅助”（AAN）范式聚焦于第二种方法，即期望控制器向用户提供最低限度的必要辅助。部分AAN系统依赖预测人类意图来调整动作。然而，若预测过于困难，反应式AAN系统可将输入指令权衡成一个涌现指令。本文提出了一种新颖的机器人抓取器AAN反应式控制系统，其中输入指令根据其各自的局部性能进行加权。因此，系统不是最小化跟踪误差或与期望速度的差异，而是根据人类的需求提供更多帮助。该系统已使用连接到敏感机器人手臂的抓取器进行测试，并提供了评估参数。测试内容包括双臂在空中完成平面路径。机器人抓住人的前臂后，路径和机器人当前位置显示在屏幕上，以向人类提供反馈。所提出的控制方法已与无辅助结果及阻抗控制进行了基准对比。统计结果表明，使用所提出的控制器，十名志愿者的全局性能得到提升，跟踪误差减少。此外，与阻抗控制不同，所提出的方法对人类施加的力、指令变化或分歧（以人类指令与输出指令之间的角度差异衡量）无显著影响。结果支持所提出的控制方案符合AAN范式，但未来工作需针对更复杂环境和任务进行进一步测试。